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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Predicting Products: Substitution vs. Elimination

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Comparing Copy Number Variations and SNPs02:26

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Related Experiment Video

Updated: Jun 29, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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A model-based approach to study nearest-neighbor influences reveals complex substitution patterns in non-coding

Guy Baele1, Yves Van de Peer, Stijn Vansteelandt

  • 1Department of Applied Mathematics and Computer Science, Ghent University, Ghent, Belgium.

Systematic Biology
|October 15, 2008
PubMed
Summary

This study introduces a new framework for modeling evolutionary site dependencies, improving evolutionary models by considering neighboring DNA bases. This approach enhances data fit and reveals complex mutation patterns like the CpG effect.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Genomics

Background:

  • Standard evolutionary models often assume site independence, which can oversimplify complex biological processes.
  • Understanding site dependencies is crucial for accurate phylogenetic inference and interpreting evolutionary dynamics.

Purpose of the Study:

  • To develop a likelihood-based framework for modeling evolutionary site dependencies across phylogenetic trees.
  • To improve the fit of evolutionary models to biological data by incorporating context-dependent evolutionary parameters.

Main Methods:

  • Developed a novel framework where evolutionary parameters depend on ancestral states of neighboring sites.
  • Employed a Markov chain Monte Carlo approach with data augmentation for parameter inference.
  • Applied the model to non-coding DNA datasets, focusing on nearest-neighbor dependencies.

Main Results:

  • The general time-reversible model with nearest-neighbor dependencies significantly improved data fit compared to site-independent models.
  • Identified context-dependent mutation processes, including the 5-methylcytosine deamination (CpG effect), influenced by neighboring bases.
  • Observed complex substitution patterns, suggesting a potential TpA effect and highlighting the complexity of dinucleotide evolution.

Conclusions:

  • Evolutionary models incorporating context-dependent effects, such as nearest-neighbor dependencies, substantially outperform independent-site models.
  • The proposed framework provides a more accurate representation of evolutionary processes, particularly in non-coding DNA.
  • Accurate modeling of site dependencies is essential for understanding mutation processes and their impact on molecular evolution.